Re: [scikit-learn] scikit-learn twitter account
I have created the https://twitter.com/sklearn_commits twitter account. I have applied to make this account a "Twitter Developer" account to be able to use https://github.com/filearts/tweethook to register it as a webhook for the main scikit-learn github repo. Once ready, I will remove the old webhook currently registered on @scikit_learn account and would like to tweet about the transfer as drafted here: https://hackmd.io/@4rHCRgfySZSdd5eMtfUJiA/H1CSpuF2S/edit Please feel free to let me know if you have any comment / suggestion about this plan. -- Olivier ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] SVM-RFE
It does not provide access for tracing the step by step feature weights and predictive ability- The user provides the n_feature. Malik --- *Prof. Malik Yousef (Associate Professor) * *The Head of the** Galilee Digital Health Research Center (GDH)* *Zefat Academic College , Department of Information System * Home Page: https://malikyousef.com/ Google Scholar Profile : https://scholar.google.com/citations?user=9UCZ_q4J=en=ao On Mon, Nov 25, 2019 at 1:36 PM Brown J.B. via scikit-learn < scikit-learn@python.org> wrote: > > 2019年11月23日(土) 2:12 Andreas Mueller : > >> I think you can also use RFECV directly without doing any wrapping. >> >> Your request to do performance checking of the steps of SVM-RFE is a >> pretty common task. >> >> > Yes, RFECV works well (and I should know as an appreciative long-time user > ;-) ), but does it actually provide a mechanism (accessors) for tracing > the step by step feature weights and predictive ability as the features are > continually reduced? > (Or perhaps it's because I'm looking at 0.20.1 and 0.21.2 > documentation...?) > > J.B. > ___ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] SVM-RFE
2019年11月23日(土) 2:12 Andreas Mueller : > I think you can also use RFECV directly without doing any wrapping. > > Your request to do performance checking of the steps of SVM-RFE is a > pretty common task. > > Yes, RFECV works well (and I should know as an appreciative long-time user ;-) ), but does it actually provide a mechanism (accessors) for tracing the step by step feature weights and predictive ability as the features are continually reduced? (Or perhaps it's because I'm looking at 0.20.1 and 0.21.2 documentation...?) J.B. ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] PolynomialFeatures
Hi Michael, Nicolas, Thank you both, that is very helpful! Best wishes Sole On Sun, 24 Nov 2019 at 03:37, Michael Eickenberg < michael.eickenb...@gmail.com> wrote: > I think it might generate a basis that is capable of generating what you > describe above, but feature expansion concretely reads as > > 1, a, b, c, a ** 2, ab, ac, b ** 2, bc, c ** 2, a ** 3, a ** 2 * b, a ** 2 > * c, a* b ** 2, abc, a*c**2, b**3, b**2 * c, b*c**2, c**3 > > Hope this helps > > On Fri, Nov 22, 2019 at 8:50 AM Sole Galli wrote: > >> Hello team, >> >> Can I double check with you that I understand correctly what the >> PolynomialFeatures() is doing under the hood? >> >> If I set it like this: >> >> poly = PolynomialFeatures(degree=3, interaction_only=False, >> include_bias=False) >> >> and I fit it on a dataset with 3 variables, a,b and c. >> >> Am I correct to say that the fit() method creates all possible >> combinations like this: >> a; >> b; >> c; >> (a+b)^2 >> (a+b)^3 >> (a+c)^2 >> (a+c)^3 >> (c+b)^2 >> (c+b)^3 >> (a+b+c)^2 >> (a+b+c)^3 >> >> And the transform() generates the expansion, without the constant that >> multiplies the interactions and avoiding duplicated terms after the >> expansion? >> >> Thanks for the help. >> >> Kind regards >> >> Sole >> >> ___ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > ___ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
